Systems responses to progressive water stress in durum wheat

Durum wheat is susceptible to terminal drought which can greatly decrease grain yield. Breeding to improve crop yield is hampered by inadequate knowledge of how the physiological and metabolic changes caused by drought are related to gene expression. To gain better insight into mechanisms defining resistance to water stress we studied the physiological and transcriptome responses of three durum breeding lines varying for yield stability under drought. Parents of a mapping population (Lahn x Cham1) and a recombinant inbred line (RIL2219) showed lowered flag leaf relative water content, water potential and photosynthesis when subjected to controlled water stress time transient experiments over a six-day period. RIL2219 lost less water and showed constitutively higher stomatal conductance, photosynthesis, transpiration, abscisic acid content and enhanced osmotic adjustment at equivalent leaf water compared to parents, thus defining a physiological strategy for high yield stability under water stress. Parallel analysis of the flag leaf transcriptome under stress uncovered global trends of early changes in regulatory pathways, reconfiguration of primary and secondary metabolism and lowered expression of transcripts in photosynthesis in all three lines. Differences in the number of genes, magnitude and profile of their expression response were also established amongst the lines with a high number belonging to regulatory pathways. In addition, we documented a large number of genes showing constitutive differences in leaf transcript expression between the genotypes at control non-stress conditions. Principal Coordinates Analysis uncovered a high level of structure in the transcriptome response to water stress in each wheat line suggesting genome-wide co-ordination of transcription. Utilising a systems-based approach of analysing the integrated wheat's response to water stress, in terms of biological robustness theory, the findings suggest that each durum line transcriptome responded to water stress in a genome-specific manner which contributes to an overall different strategy of resistance to water stress

Automatic wheat ear counting using machine learning based on RGB UAV imagery

In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield‐determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear‐counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017–2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro‐plot images. The relationship between the image‐based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in‐situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in‐situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed

Automatic wheat ear counting using machine learning based on RGB UAV imagery

In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is no standard protocol for wheat ear counting in the field, and moreover it is time consuming. An automatic ear-counting system is proposed using machine learning techniques based on RGB (red, green, blue) images acquired from an unmanned aerial vehicle (UAV). Evaluation was performed on a set of 12 winter wheat cultivars with three nitrogen treatments during the 2017-2018 crop season. The automatic system uses a frequency filter, segmentation and feature extraction, with different classification techniques, to discriminate wheat ears in micro-plot images. The relationship between the image-based manual counting and the algorithm counting exhibited high levels of accuracy and efficiency. In addition, manual ear counting was conducted in the field for secondary validation. The correlations between the automatic and the manual in-situ ear counting with grain yield were also compared. Correlations between the automatic ear counting and grain yield were stronger than those between manual in-situ counting and GY, particularly for the lower nitrogen treatment. Methodological requirements and limitations are discussed.status: publishe

Do metabolic changes underpin physiological responses to water limitation in alfalfa (Medicago sativa) plants during a regrowth period?

Drought is one of the most limiting factors on crop productivity under Mediterranean conditions, where the leguminous species alfalfa (Medicago sativa L.) is extensively cultivated. Whereas the effect of drought on plant performance has been widely described at leaf and nodule levels, less attention has been given to plant-nodule interactions and their implication on metabolites exchange during a regrowth period, when water is limiting. For this purpose, physiological characterization and metabolite profiles in different plant organs and nodules were undertaken under water deficit, including regrowth after removal of aerial parts. In order to study in more detail how nitrogen (N) metabolism was affected by water stress, plants were labelled with N-enriched isotopic air (15N2) using especially designed chambers. Water stress affected negatively water status and photosynthetic machinery. Metabolite profile and isotopic composition analyses revealed that, water deficit induced major changes in the accumulation of amino acids (proline, asparagine, histidine, lysine and cysteine), carbohydrates (sucrose, xylose and pinitol) and organic acids (fumarate, succinate and maleic acid) in the nodules in comparison with other organs. The lower 15N-labeling observed in serine, compared with other amino acids, was related with its high turnover rate, which in turn, indicates its potential implication in photorespiration. Isotopic analysis of amino acids also revealed that proline synthesis in the nodule was a local response to water stress and not associated with a feedback inhibition from the leaves. Water deficit induced extensive reprogramming of whole-plant C and N metabolism, including when the aerial part was removed to trigger regrowth.This study was supported in part by the European research project PERMED (INCO- CT-2004-509140), the Spanish Ministry of Science and Technology (CGL2009-13079-C02) and i-COOP-suelos y legumbres (2016SU0016) funded by the National Research Council. GM was the recipient of a doctoral fellowship (Programa Nacional de Formación de Personal Universitario, AP2005–2916) sponsored by the Spanish Ministry of Education and Science

UAV and Ground Image-Based Phenotyping: A Proof of Concept with Durum Wheat

Climate change is one of the primary culprits behind the restraint in the increase of cereal crop yields. In order to address its effects, effort has been focused on understanding the interaction between genotypic performance and the environment. Recent advances in unmanned aerial vehicles (UAV) have enabled the assembly of imaging sensors into precision aerial phenotyping platforms, so that a large number of plots can be screened effectively and rapidly. However, ground evaluations may still be an alternative in terms of cost and resolution. We compared the performance of red−green−blue (RGB), multispectral, and thermal data of individual plots captured from the ground and taken from a UAV, to assess genotypic differences in yield. Our results showed that crop vigor, together with the quantity and duration of green biomass that contributed to grain filling, were critical phenotypic traits for the selection of germplasm that is better adapted to present and future Mediterranean conditions. In this sense, the use of RGB images is presented as a powerful and low-cost approach for assessing crop performance. For example, broad sense heritability for some RGB indices was clearly higher than that of grain yield in the support irrigation (four times), rainfed (by 50%), and late planting (10%). Moreover, there wasn’t any significant effect from platform proximity (distance between the sensor and crop canopy) on the vegetation indexes, and both ground and aerial measurements performed similarly in assessing yield

Comparative UAV and Field Phenotyping to Assess Yield and Nitrogen Use Efficiency in Hybrid and Conventional Barley

With the commercialization and increasing availability of Unmanned Aerial Vehicles (UAVs) multiple rotor copters have expanded rapidly in plant phenotyping studies with their ability to provide clear, high resolution images. As such, the traditional bottleneck of plant phenotyping has shifted from data collection to data processing. Fortunately, the necessarily controlled and repetitive design of plant phenotyping allows for the development of semi-automatic computer processing tools that may sufficiently reduce the time spent in data extraction. Here we present a comparison of UAV and field based high throughput plant phenotyping (HTPP) using the free, open-source image analysis software FIJI (Fiji is just ImageJ) using RGB (conventional digital cameras), multispectral and thermal aerial imagery in combination with a matching suite of ground sensors in a study of two hybrids and one conventional barely variety with ten different nitrogen treatments, combining different fertilization levels and application schedules. A detailed correlation network for physiological traits and exploration of the data comparing between treatments and varieties provided insights into crop performance under different management scenarios. Multivariate regression models explained 77.8, 71.6, and 82.7% of the variance in yield from aerial, ground, and combined data sets, respectively

Differential CO2 effect on primary carbon metabolism of flag leaves in durum wheat (Triticum durum Desf.)

International audienceC sink/source balance and N assimilation have been identified as target processes conditioning crop responsiveness to elevated CO2 . However, little is known about phenology-driven modifications of C and N primary metabolism at elevated CO2 in cereals such as wheat. Here, we examined the differential effect of elevated CO2 at two development stages (onset of flowering, onset of grain filling) in durum wheat (Triticum durum, var. Sula) using physiological measurements (photosynthesis, isotopes), metabolomics, proteomics and (15) N labelling. Our results show that growth at elevated CO2 was accompanied by photosynthetic acclimation through a lower internal (mesophyll) conductance but no significant effect on Rubisco content, maximal carboxylation or electron transfer. Growth at elevated CO2 altered photosynthate export and tended to accelerate leaf N remobilization, which was visible for several proteins and amino acids, as well as lysine degradation metabolism. However, grain biomass produced at elevated CO2 was larger and less N rich, suggesting that nitrogen use efficiency rather than photosynthesis is an important target for improvement, even in good CO2 -responsive cultivars

Overview of cell function transcript expression during the water stress transient.

<p>Transcript profile changes, at decreasing leaf %RWC (82,74,66,58), were similar across the stress transient for the three wheat lines in probes taken from MapMan bins 15,17,20,21,26,27,28,29,30,31,33,34 in the <i>Line</i>+<i>Stress</i> ANOVA group dataset (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0108431#pone.0108431.s005" target="_blank">File S4</a>). Results were visualised in MapMan and the colour scale for transcripts that were increased or decreased in abundance were denoted as red and green, respectively.</p